scholarly journals Actual and Forecasted Vulnerability Assessment to Seawater Intrusion via GALDIT-SUSI in the Volturno River Mouth (Italy)

2021 ◽  
Vol 13 (18) ◽  
pp. 3632
Author(s):  
Gianluigi Busico ◽  
Carla Buffardi ◽  
Maria Margarita Ntona ◽  
Marco Vigliotti ◽  
Nicolò Colombani ◽  
...  
Keyword(s):  
Seawater Intrusion ◽  
Drainage System ◽  
River Mouth ◽  
Surface Model ◽  
Land Use Classification ◽  
Groundwater Salinization ◽  
Future Assessment ◽  
Input Variables ◽  
Recharge Rates ◽  
Volturno River

Coastal areas have become increasingly vulnerable to groundwater salinization, especially in the last century, due to the combined effects of climate change and growing anthropization. In this study, a novel methodology named GALDIT-SUSI was applied in the floodplain of the Volturno River mouth for the current (2018) and future (2050) evaluation of seawater intrusion accounting for the expected subsidence and groundwater salinization rates. Several input variables such as digital surface model, land use classification, subsidence rate and drainage system have been mapped via remote sensing resources. The current assessment highlights how areas affected by salinization coincide with the semiperennial lagoons and inland depressed areas where paleosaline groundwaters are present. The future assessment (2050) shows a marked increase of salinization vulnerability in the coastal strip and in the most depressed areas. The results highlight that the main vulnerability driver is the Revelle index, while predicted subsidence and recharge rates will only slightly affect groundwater salinization. This case study indicates that GALDIT-SUSI is a reliable and easy-to-use tool for the assessment of groundwater salinization in many coastal regions of the world.

scholarly journals Bioethanol Production from an Underutilized Plant, Calabash (Crescentia Cujete) Using Co-Culture of Saccharomyces Cerevisiae and Cronobacter Malonaticus

2021 ◽  
pp. 17-33
Author(s):  
N. U. Nwogwugwu ◽  
G.O. Abu ◽  
O. Akaranta
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Response Surface ◽  
Reducing Sugars ◽  
Surface Model ◽  
Wide Range ◽  
Crescentia Cujete ◽  
Input Variables ◽  
Ph Range ◽  
Rsm Model ◽  
Clear Solution

Response surface methodology (RSM) model was used to optimize ethanol production from calabash (Crescentia cujete) pulp juice using co-culture of Saccharomyces cerevisiae and Cronobacter malonaticus. The calabash pulp was squeezed with muslin cloth, and vacuum filtered to clear solution before use. The clear juice was tested for reducing sugars using the Dinitrosalicylic acid (DNS) method. Twenty three runs (23), including 3 controls, of the fermentation were conducted at varying temperatures, pH, and volumes of inoculum. The process parameters (input variables): volumes of inoculum, temperature, and pH were subjected to response surface model, using the Central composite design (CCD). Fermentation was done in conical flasks covered with cotton wool and foil in a stationary incubator for four days (96 hours). Active co-culture of Saccharomyces cerevisiae and Cronobacter malonaticus was used, with inoculum developed using Marcfaland’s method. Samples were collected every 24 hours, centrifuged, filtered and analyzed for measurement of the output variables: reducing sugar, cell density and ethanol concentration. The concentration of reducing sugars from Calabash pulp was 3.2 mg/ml. Results obtained also revealed that the fermentation can take place on a wide range of temperature; 29-31.60C . The optimal pH range for performance of the co-culture for the fermentation process was pH range 7.9- 8.0. The optimum volume of inoculum was 5.5%v/v (ie 5.5 ml in 94.5ml juice). The optimized process using the RSM model gave 6.97% v/v bioethanol at 29oC and pH 7.9. The bioethanol yield from Calabash substrate is reasonable with co-culture considering the concentration of reducing sugars obtained from the juice and the duration of the fermentation.

scholarly journals Application of Response Surface Methodology for Optimizing the Production of Bioethanol from Calabash (Crescentia cujete) Substrate Using Saccharomyces cerevisiae

2019 ◽  
pp. 1-12 ◽  
Author(s):  
N. U. Nwogwugwu ◽  
G. O. Abu ◽  
O. Akaranta ◽  
E. C. Chinakwe
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Reducing Sugars ◽  
Surface Model ◽  
Wide Range ◽  
Crescentia Cujete ◽  
Input Variables ◽  
Ph Range ◽  
Rsm Model

Aim: The study employed the Response surface methodology (RSM) model to optimize ethanol production from Calabash (Crescentia cujete) pulp juice using Saccharomyces cerevisiae. Study Design: The Calabash pulp was squeezed with muslin cloth, and vacuum filtered to clear solution before use. The clear juice was tested for reducing sugars using the Dinitrosalicylic acid (DNS) method. Twenty three (23) runs, including 3 controls, of the fermentation was conducted at varying temperatures, pH, and volumes of inoculum.The process parameters (input variables): volumes of inoculum, temperature,and pH were subjected to response surface model, using the Central Composite Design (CCD). Place and Duration of Study: This study was carried out in the Environmental Microbiology Laboratory, University of Port Harcourt for six months. Methodology: Fermentation was done in conical flasks covered with cotton wool and foil in a stationary incubator for four days (96 hours). Active stock culture of Saccharomyces cerevisiae was used, with inoculum developed using Marcfaland’s method. Samples were collected every 24 hours, centrifuged, filtered and analyzed for measurement of the output variables: Reducing sugar, cell density and ethanol concentration. Results: The concentration of reducing sugars from Calabash pulp was 3.2 mg/ml. Results obtained also revealed that the fermentation can take place on a wide range of temperature 25-40°C. The optimal pH range for performance of S. cerevisiae for the fermentation process was pH 5.0-6.5. The optimum volume of inoculum was 5.5%v/v (ie 5.5 ml in 94.5ml juice). The optimized process using the RSM model gave 6.19% v/v bioethanol. Control: The bioethanol yield from Calabash substrate is reasonable considering the concentration of reducing sugars obtained from the juice and the duration of the fermentation.

Groundwater salinization and seawater intrusion tracing based on Lithium concentration in the shallow aquifer of Jerba Island, southeastern Tunisia

2018 ◽  
Vol 138 ◽  
pp. 233-246 ◽  
Author(s):  
Faiza Souid ◽  
Belgacem Agoubi ◽  
Faten Telahigue ◽  
Abdelkader Chahlaoui ◽  
Adel Kharroubi
Keyword(s):  
Seawater Intrusion ◽  
Lithium Concentration ◽  
Shallow Aquifer ◽  
Groundwater Salinization

Environmental isotopic and hydrochemical characteristics of groundwater from the Sandspruit Catchment, Berg River Basin, South Africa

2013 ◽  
Vol 69 (3) ◽  
pp. 601-611 ◽  
Author(s):  
S. Naicker ◽  
M. Demlie
Keyword(s):  
Drainage System ◽  
Environmental Isotopes ◽  
Balance Method ◽  
Coupled Analysis ◽  
Saline Groundwater ◽  
Stable Isotope Signatures ◽  
Multi Temporal ◽  
Recharge Rates ◽  
Rainfall Region ◽  
Isotopic Signal

The Sandspruit catchment (a tributary of the Berg River) represents a drainage system, whereby saline groundwater with total dissolved solids (TDS) up to 10,870 mg/l, and electrical conductivity (EC) up to 2,140 mS/m has been documented. The catchment belongs to the winter rainfall region with precipitation seldom exceeding 400 mm/yr, as such, groundwater recharge occurs predominantly from May to August. Recharge estimation using the catchment water-balance method, chloride mass balance method, and qualified guesses produced recharge rates between 8 and 70 mm/yr. To understand the origin, occurrence and dynamics of the saline groundwater, a coupled analysis of major ion hydrochemistry and environmental isotopes (δ18O, δ2H and 3H) data supported by conventional hydrogeological information has been undertaken. These spatial and multi-temporal hydrochemical and environmental isotope data provided insight into the origin, mechanisms and spatial evolution of the groundwater salinity. These data also illustrate that the saline groundwater within the catchment can be attributed to the combined effects of evaporation, salt dissolution, and groundwater mixing. The salinity of the groundwater tends to vary seasonally and evolves in the direction of groundwater flow. The stable isotope signatures further indicate two possible mechanisms of recharge; namely, (1) a slow diffuse type modern recharge through a relatively low permeability material as explained by heavy isotope signal and (2) a relatively quick recharge prior to evaporation from a distant high altitude source as explained by the relatively depleted isotopic signal and sub-modern to old tritium values.

Investigation of Approximation Methods for Mechanism Reliability Analysis Considered Nonlinear Response

2014 ◽  
Vol 635-637 ◽  
pp. 457-461
Author(s):  
Hui Wang
Keyword(s):  
Reliability Analysis ◽  
Nonlinear Dynamic ◽  
Kriging Model ◽  
Dynamic Responses ◽  
Joint Clearance ◽  
Support Vector ◽  
Surface Model ◽  
Key Factors ◽  
Input Variables ◽  
Abnormal Point

The joint clearance and contact phenomena are the key factors which can induce the nonlinear dynamic responses and affect the performance of the practical mechanism. It is difficult to solve the nonlinear relations between the input variables and responses when the random factors are considered. In this paper, three approximation models, i.e., Response Surface Model (RSM), Kriging model and Support Vector Machine (SVM), are selected to investigate the reliability analysis procedure by the combination of simulation technique and approximation model for the mechanism with joint clearance. The solution strategy with supplemental samples is proposed to solve the problem caused by the occurrence of the abnormal point in the iteration procedure, and to avoid the divergence in the reliability analysis caused by the nonlinear dynamic responses of the mechanism. Meanwhile, the computational consumption can be reduced as the results of the known samples are reused. Finally, the reliability analysis for a slider-crank mechanism with joint clearance is utilized to illustrate the proposed procedure.

scholarly journals Response surface methodology and optimization of the processes for bioethanol production from Calabash (Crescentia cujete) Using Cronobacter malonaticus

2021 ◽  
Vol 14 (2) ◽  
pp. 204-216
Author(s):  
Ngozi Ursulla Nwogwugwu ◽  
Gideon O. Abu ◽  
Onyewuchi Akaranta ◽  
Ettienne C. Chinakwe ◽  
Ikenna N.Nwachukwu ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Ethanol Production ◽  
Response Surface ◽  
Reducing Sugars ◽  
Surface Model ◽  
Wide Range ◽  
Crescentia Cujete ◽  
Input Variables ◽  
Ph Range ◽  
Rsm Model

Aim: Response surface methodology (RSM) model was applied to optimize ethanol production from Calabash (Crescentia cujete) pulp juice using Cronobacter malonaticus. Study Design: The Calabash pulp was squeezed with muslin cloth, and vacuum filtered to clear solution before use. The clear juice was tested for reducing sugars using the Dinitrosalicylic acid (DNS) method. Twenty three (23) runs, including 3 controls, of the fermentation was conducted at varying temperatures, pH, and volumes of inoculum. The process parameters (input variables): volumes of inoculum, temperature, and pH were subjected to response surface model, using the Central Composite Design (CCD). Place and Duration of Study: This study was carried out in the Environmental Microbiology Laboratory, University of Port Harcourt for six months. Methodology: Fermentation was done in conical flasks covered with cotton wool and foil in a stationary incubator for four days (96 hours). Active stock culture of Cronobacter malonaticus was used, with inoculum developed using Marcfaland’s method. Samples were collected every 24 hours, centrifuged, filtered and analyzed for measurement of the output variables: Reducing sugar, cell density and ethanol concentration. Results: The concentration of reducing sugars from Calabash pulp was 3.2 mg/ml. Results obtained also revealed that the fermentation can take place on a wide range of temperature 28-32°C. The optimal pH range for performance of C.malonaticus for the fermentation process was pH 5.95-6.5. The optimum volume of inoculum was 10%v/v (i.e. 10 ml in 90 ml juice). The optimized process using the RSM model gave 5.08% v/v bioethanol, being the highest achieved at pH6.08 and 28oC . Conclusion: The bioethanol yield from Calabash substrate is reasonable considering that the bacterium used is not known for ethanol production. Also the concentration of reducing sugars in the substrate and the duration of fermentation could be responsible for the yield.

A study on the impact of anthropogenic and geogenic factors on groundwater salinization and seawater intrusion in Gaza coastal aquifer, Palestine: An integrated multi-techniques approach

2019 ◽  
Vol 156 ◽  
pp. 75-93 ◽  
Author(s):  
Madhat Farouk Abu Al Naeem ◽  
Ismail Yusoff ◽  
Tham Fatt Ng ◽  
Jyoti Prakash Maity ◽  
Yatimah Alias ◽  
...  
Keyword(s):  
Seawater Intrusion ◽  
Coastal Aquifer ◽  
Groundwater Salinization ◽  
The Impact

scholarly journals ANALYSIS AND VALIDATION OF GRID DEM GENERATION BASED ON GAUSSIAN MARKOV RANDOM FIELD

2016 ◽  
Vol XLI-B2 ◽  
pp. 277-284 ◽  
Author(s):  
F. J. Aguilar ◽  
M. A. Aguilar ◽  
J. L. Blanco ◽  
A. Nemmaoui ◽  
A. M. García Lorca
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Digital Terrain Model ◽  
Linear Interpolation ◽  
Surface Model ◽  
Lidar Data ◽  
Mathematical Framework ◽  
Land Use Classification ◽  
Gaussian Markov Random Field ◽  
Markov Random

Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m<sup>2</sup>) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.

scholarly journals Subsidence Trends of Volturno River Coastal Plain (Northern Campania, southern Italy) Inferred by SAR Interferometry Data

2017 ◽  
Author(s):  
Fabio Matano ◽  
Marco Sacchi ◽  
Marco Vigliotti ◽  
Daniela Ruberti
Keyword(s):  
Ground Deformation ◽  
Research Work ◽  
River Mouth ◽  
Alluvial Plain ◽  
Sar Interferometry ◽  
Coastal Hazards ◽  
Tectonic Activity ◽  
Post Processing ◽  
Persistent Scatterer Interferometry ◽  
Volturno River

The Volturno Plain is one of the largest alluvial plain of peninsular Italy. This area is characterized by both natural and human induced subsidence, and is and most susceptible to coastal hazards. The present study is based on post-processing, analysis and mapping of the available Persistent Scatterer interferometry datasets, derived from combination of both ascending and descending orbits of three different SAR satellite systems, during an observation period of almost two decades (June 1992 - September 2010). The main output of the research work is a map of the vertical deformation that provides new insights into the areal variability of ground deformation processes (subsidence/uplift) of Volturno plain over the last decades. Vertical displacement values derived by interferometric data post-processing show that the Volturno river plain is characterized by significant subsidence in the central axial sectors and in the river mouth area, whereas moderate uplift is detected in the eastern part of the plain. Other sectors of the study area are characterized by moderate subsidence and/or stability. We infer that the subsidence recorded in  the Volturno plain is mainly a consequence of a natural process related to the compaction of the fluvial deposits that fill up the alluvial plain. Anthropic influence (e.g. water exploitation, urbanization) can be substantially regarded as an additional factor that only locally may enhance subsidence. The uplift imaged in the eastern sector of the plain can be related to tectonic activity. The study of subsidence in the Volturno plain is a valuable tool relevant for river flood analyses and coastal inundation hazard assessment addressed to risk mitigation.

scholarly journals IMPROVING THE CLASSIFICATION OF LAND USE OBJECTS USING DENSE CONNECTIVITY OF CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 667-673
Author(s):  
A. Gujrathi ◽  
C. Yang ◽  
F. Rottensteiner ◽  
K. M. Buddhiraju ◽  
C. Heipke
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Convolutional Neural Networks ◽  
Spatial Databases ◽  
Aerial Images ◽  
Automatic Process ◽  
Surface Model ◽  
Land Use Classification ◽  
Global Average

Abstract. Land use is an important variable in remote sensing which describes the functions carried out on a piece of land in order to obtain benefits and is especially useful to the personnel working in the fields of urban management and planning. The land use information is maintained by national mapping agencies in geo-spatial databases. Commonly, land use data is stored in the form of polygon objects; the label of the object indicates land use. The main goal of classification of land use objects is to update an existing database in an automatic process. Recently, Convolutional Neural Networks (CNN) have been widely used to tackle this task utilizing high resolution aerial images (and derived data such as digital surface model). One big challenge classifying polygons is to deal with the large variation in their geometrical extent. For this challenge, we adopt the method of Yang et al. (2019) to decompose polygons into regular patches of fixed size. The decomposition leads to two sets of polygons: small and large, where the former suffers from a lower identification rate. In this paper, we propose CNN methods which incorporate dense connectivity and integrate it with intermediate information via global average pooling to improve land use classification, mainly focusing on small polygons. We present different network variants by incorporating intermediate information via global average pooling from different stages of the network. We test our methods on two sites; our experiments show that the dense connectivity and integration of intermediate information has a positive effect not only on the classification accuracy on the whole but also on the identification of small polygons.

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